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Communication, Sensing and Control integrated Closed-loop System: Modeling, Control Design and Resource Allocation

Zeyang Meng, Dingyou Ma, Zhiqing Wei, Ying Zhou, Zhiyong Feng

TL;DR

This work tackles the gap in modeling industrial closed-loop systems by integrating sensing, communication, and control with an uplink–downlink coupled framework through effective-capacity theory. It derives practical metrics for closed-loop delay $D_c$, packet loss $\\epsilon_c$, and the maximum arrival rate $\\lambda_{\\max}$, and establishes a Lyapunov-based inequality that links convergence rate $\\rho$ to sensing, communication, and estimation performance. A non-convex joint optimization is proposed to jointly design the control law and wireless resources, solved globally via differential evolution, and validated through simulations on an AGV scenario that reveal nonlinear tradeoffs among delay, quantization, convergence, and control energy. The results provide actionable insights for the co-design of communication, sensing, and control in industrial wireless networks, highlighting how overly aggressive or lax parameter choices can degrade performance due to their interdependencies.

Abstract

The wireless communication technologies have fundamentally revolutionized industrial operations. The operation of the automated equipment is conducted in a closed-loop manner, where the status of devices is collected and sent to the control center through the uplink channel, and the control center sends the calculated control commands back to the devices via downlink communication. However, existing studies neglect the interdependent relationship between uplink and downlink communications, and there is an absence of a unified approach to model the communication, sensing, and control within the loop. This can lead to inaccurate performance assessments, ultimately hindering the ability to provide guidance for the design of practical systems. Therefore, this paper introduces an integrated closed-loop model that encompasses sensing, communication, and control functionalities, while addressing the coupling effects between uplink and downlink communications. Through the analysis of system convergence, an inequality pertaining to the performances of sensing, communication, and control is derived. Additionally, a joint optimization algorithm for control and resource allocation is proposed. Simulation results are presented to offer an intuitive understanding of the impact of system parameters. The findings of this paper unveil the intricate correlation among sensing, communication, and control, providing insights for the optimal design of industrial closed-loop systems.

Communication, Sensing and Control integrated Closed-loop System: Modeling, Control Design and Resource Allocation

TL;DR

This work tackles the gap in modeling industrial closed-loop systems by integrating sensing, communication, and control with an uplink–downlink coupled framework through effective-capacity theory. It derives practical metrics for closed-loop delay , packet loss , and the maximum arrival rate , and establishes a Lyapunov-based inequality that links convergence rate to sensing, communication, and estimation performance. A non-convex joint optimization is proposed to jointly design the control law and wireless resources, solved globally via differential evolution, and validated through simulations on an AGV scenario that reveal nonlinear tradeoffs among delay, quantization, convergence, and control energy. The results provide actionable insights for the co-design of communication, sensing, and control in industrial wireless networks, highlighting how overly aggressive or lax parameter choices can degrade performance due to their interdependencies.

Abstract

The wireless communication technologies have fundamentally revolutionized industrial operations. The operation of the automated equipment is conducted in a closed-loop manner, where the status of devices is collected and sent to the control center through the uplink channel, and the control center sends the calculated control commands back to the devices via downlink communication. However, existing studies neglect the interdependent relationship between uplink and downlink communications, and there is an absence of a unified approach to model the communication, sensing, and control within the loop. This can lead to inaccurate performance assessments, ultimately hindering the ability to provide guidance for the design of practical systems. Therefore, this paper introduces an integrated closed-loop model that encompasses sensing, communication, and control functionalities, while addressing the coupling effects between uplink and downlink communications. Through the analysis of system convergence, an inequality pertaining to the performances of sensing, communication, and control is derived. Additionally, a joint optimization algorithm for control and resource allocation is proposed. Simulation results are presented to offer an intuitive understanding of the impact of system parameters. The findings of this paper unveil the intricate correlation among sensing, communication, and control, providing insights for the optimal design of industrial closed-loop systems.
Paper Structure (21 sections, 4 theorems, 64 equations, 6 figures, 2 tables, 1 algorithm)

This paper contains 21 sections, 4 theorems, 64 equations, 6 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Communication Model
  4. The Single Queue Effective Capacity Model
  5. The Analysis of the Tandem Queue
  6. Control Model
  7. The Basic Control Model
  8. Control Model Considering Imperfect Sensing And Wireless Communication
  9. Sensing and Estimation Model
  10. Convergence Analysis of the Closed-loop System
  11. The Optimization of Control Strategy and Resource Allocation
  12. Simulation Results
  13. The Impact of Closed-Loop Delay
  14. The Impact of Convergence Rate
  15. The Impact of Quantization Level
  16. ...and 6 more sections

Key Result

Theorem 1

The effective capacity of uplink and downlink queues under Assumption 3 is approximated by where $\beta_i$ is the rate parameter of the random variable $\gamma^2$ in the uplink and downlink channels.

Figures (6)

  • Figure 1: A typical closed-loop process in the typical industry with wireless network.
  • Figure 2: System model of a wireless closed-loop control system.
  • Figure 3: The delay-compensation control method.
  • Figure 4: Variations of the control cost, state distance, accumulated cost and accumulated control energy with closed-loop delay constraints.
  • Figure 5: Variations of the control cost, state distance, accumulated cost and accumulated control energy with different convergence rate.
  • ...and 1 more figures

Theorems & Definitions (4)

  • Theorem 1: Effective Capacity of Rayleigh Channel
  • Theorem 2: Packet Loss Rate and Closed-loop Delay
  • Theorem 3: The Maximum Arrival Rate
  • Theorem 4: Convergence Rate Inequality